Soft Sensing for Time Series With Irregular Sampling Internals Based on a Denoising Interval Attention LSTM Network

The prediction of key quality variables plays an important role in industrial status identification and monitoring. Due to process disturbance and hard device limitation, data collection in modern industries often exhibits high noise and irregular data sampling. To solve the above problems, this art...

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Vydáno v:	IEEE transaction on neural networks and learning systems Ročník PP; s. 1 - 13
Hlavní autoři:	He, Yuchen, Yang, Xueqin, Qian, Lijuan, Yao, Le, Ye, Lingjian, Wu, Ping, Ye, Gangyue, Ye, Weirong, Shen, Yafang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 21.08.2025
Témata:	Adaptation models Autoencoders Data mining Feature extraction Interval attention LSTM (IALSTM) irregular sampling Long short term memory Noise Noise reduction quality-related information Real-time systems soft sensing Soft sensors stacked supervised and reconstructed input denoising autoencoder (SSRDAE) Time series analysis
ISSN:	2162-237X, 2162-2388, 2162-2388
On-line přístup:	Získat plný text
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